Kashish Ara Shakil

A survey on scholarly data

Survey of big scholarly data with respect to the different phases of the big data lifecycle.Identifies the different big data tools and technologies that can be used for development of scholarly applications.Investigates research challenges and limitations specific to big scholarly data and its applications.Provides research directions and paves way towards the development of a generic and comprehensive big scholarly data platform. Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle.

Cloud-Based Big Data Analytics—A Survey of Current Research and Future Directions

The advent of the digital age has led to a rise in different types of data with every passing day. In fact, it is expected that half of the total data will be on the cloud by 2016. This data is complex and needs to be stored, processed, and analyzed for information that can be used by organizations. Cloud computing provides an apt platform for big data analytics in view of the storage and computing requirements of the latter. This makes cloud-based analytics a viable research field. However, several issues need to be addressed and risks need to be mitigated before practical applications of this synergistic model can be popularly used. This paper explores the existing research, challenges, open issues, and future research direction for this field of study.

Cloud Computing in Bioinformatics and Big Data Analytics: Current Status and Future Research

Bioinformatics research involves a huge amount of data which is complex in nature. It also involves analysis of huge data sets. Conventional techniques used in bioinformatics takes a lot of time to get results and also it’s difficult to analyze the complex nature of data involved. Therefore, machines having huge processing capabilities are required leading to an escalation in the amount of money which is required do research in bioinformatics field. The problems faced by bioinformatics researchers in order to carry out their research in an economic and fast manner can be solved easily with the help of Cloud computing concepts. Thus, cloud computing is a boon for bioinformatics research. In this paper, we have discussed how the cloud computing will be helpful for bioinformatics researchers ultimately acting as a stepping stone towards big data analytics. It also explains about the current state of the art in bioinformatics and big data analytics and potential future research issues that need to be addressed.

Educational intelligence: Applying cloud-based big data analytics to the Indian education sector

Big Data technology is a generic technology, which can be applied to any real-world problem that involves a lot of data. Moreover, the use of cloud-based infrastructure to implement the big data technology makes it a cost-effective solution to the big data problem. One of the fundamental sectors that can benefit from this technology is education and research. The education system can use big data analytics to provide better education and administer the institutional operations. Research, which is an extension of education, can use analytics of big scholar data, for diverse applications, to facilitate research at the individual, team and organization level. With that said, the practical implementation and adoption of big data for education and research, collectively referred to as ‘Educational Intelligence’, faces several challenges, particularly in a developing country like India. This research paper explores how cloud-based big data analytics can be applied to Indian education and research and reviews the challenges that need to be addressed before the true benefits of this technology can be obtained.